SBIR Phase II: Automated Structural Health Monitoring Sensor

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 0724434
Agency Tracking Number: 0539234
Amount: $499,838.00
Phase: Phase II
Program: SBIR
Awards Year: 2007
Solicitation Year: 2005
Solicitation Topic Code: EL
Solicitation Number: NSF 05-557
Small Business Information
LOS GATOS RESEARCH INC
67 E EVELYN AVE STE 3, Suite 100, MOUNTAIN VIEW, CA, 94041
DUNS: 928805761
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 An-Dien Nguyen
 PhD
 (650) 965-3459
 a.d.nguyen@lgrinc.com
Business Contact
 An-Dien Nguyen
Title: PhD
Phone: (650) 965-3459
Email: a.d.nguyen@lgrinc.com
Research Institution
N/A
Abstract
This Small Business Innovation Research (SBIR) Phase II research project will support the development of a new automated structural health monitoring (SHM) sensor system capable of detecting cracks and measuring stress in advanced structures. Current electrically-based SHM instrument is bulky and expensive. With the increase of sensing points and structure size, the amount of cabling, weight, and cost for hardware increases dramatically. This instrument combines optical waveguides and fibers, and Bragg Gratings (BG) with a low-cost, rugged light source to yield a SHM instrument capable of continuous measurements in the field with high precision and sensitivity. Phase II research will develop a field-tested 8-channel BG-based SHM instrument for simultaneous crack detection and loading stress measurements in large structures. This novel SHM instrumentation will offer significant cost saving by providing a low cost solution for crack detection in large airframe structures such as wings, fuselage, and lap joints, as well as in civil structures such as oil pipelines, bridges, freeways, plants and buildings. The new sensor technology will enhance public safety as a result of low-cost condition-based maintenance and effective warning systems due to the sensor instrument's accurate prognosis and early prediction of catastrophic failures in large public transportation and utility systems.

* information listed above is at the time of submission.

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